Session ONE: Big Data and Disruptive Innovation

  • Exploring the latest trends in the big data space
  • Impact of data on business models and profitability
  • Disruptive technologies like AI, Machine Learning, IoT and their application to solving business problems
  • Leading large scale data-driven transformation across your enterprise
  • Achieving ROI from your data projects
  • Moving to real-time analysis for better responsiveness and forecasting
  • Security, compliance, privacy and transparent use of data
  • Turning data into new visibility and business intelligence
  • Utilising predictive analytics for impactful action
  • Widening data applications to achieve specific business outcomes – from personalised customer journeys to marketing and more
Conference Chair’s Opening Address
Opening Keynote Address: Privacy, Security, and Bias in analysis of Big Data Sets

Dr Louise Bennett, Co-Chair, Privacy and Consumer Advisory Group; Director, Digital Policy Alliance

This talk will consider the key learning points from experience of data analytics over the last 50 years covering:

  • Analytic objectives
  • Data quality and relevance
  • The analyst’s understanding of their data
  • The confusion between correlation and causation
  • Analysis that grows like Topsy
  • Understanding bias
Creating A Data-Driven Culture

Siloed traditional models, inability to understand the immense amount of data, borderline Data IQ offices are growing issues. There is an increasing need for data and analytics leaders to follow the example of English as a second language and treat information as the new second language for business.

  • Data Dexterity – Identifying language gaps and establishing an ISL proof of concept for language development
  • How can leaders (e.g. CDO) become the boosters of curiosity and critical thinking in the workforce
  • How to create office spaces that take advantage of the potential of a data-driven workplace
  • Making data accessible across the enterprise, integrating your data across siloed functions
  • Establishing connections between your data and business objectives
  • Using data to help make informed decisions
Building a ‘Greenfield’ data capability at Apetito

Sudesh Jog, Head of Analytics, Apetito

Over the course of three years, Sudesh built the data function at Apetito from scratch.

Based in Wiltshire, Apetito provides nutritious meals for people at home or in hospitals or care homes. The business has rich customer data but was not leveraging the opportunity that this data represented.

In his presentation, Sudesh will talk about the journey of getting data to the heart of strategic decision making at Apetito and will also share his experience and insights about-

  • Developing a data centric culture
  • Getting the buy-in for investment in data
  • Building the team
  • Evolving the technology infrastructure
Engaging Business Leaders with Storytelling

Do you want to tell a better story with your data? Would you like your data visualisation to lead to a better understanding of your business and provide some actionable insights? In this session, we consider how data visualisation can be a valuable social currency in your enterprise, allowing business functions to share insights and make new discoveries. We discuss:

  • When to use data visualisation
  • Understanding context and target audience
  • Choosing effective visualisation tools
  • Essential criteria of a compelling story
From Spark to Tensorflow, how to build an end to end ML pipeline?

Florian Dejax, Data Scientist – Assistant Vice President, Barclaycard

  • Use Spark / Tensorflow to complete some stages in a ML workflow
  • Integrating seamlessly Spark and TensorFlow
  • Use multi GPU to train a deep learning model at scale
Questions To The Panel Of Speakers
Refreshment Break Served in the Exhibition Area
On-Premises Vs Cloud for Data Infrastructure

Berekmeri Mihaly, Lead Big Data Developer, Equifax

Cloud computing has gained popularity due to the time and money-saving improvements it offers, and it is here to stay. Cloud as a platform for databases is also growing at high-speed thanks to a vast range of cloud hosting services and dbPaaS. In this session, we consider:

  • Why and how to move your data infrastructure to the Cloud: weighing the risks and benefits
  • Different cloud platforms for your databases, how to choose the right vendor
  • The TCO of cloud data infrastructure
Selecting the Core of your Data and Analytics Platform

Data is growing at a fast pace, and so have the number of storage options. Data lakes, Data hubs and Data warehouse have similar core functions, and they are often mistakenly understood as interchangeable. The reality is that they usually store different types of data, have different data standards and use diverse data systems. This is why it is essential to pick the right core for your enterprise. We discuss:

  • Understanding the differences between hubs, lakes and warehouses
  • Assessing what fits best for the data you want to store (e.g. flexibility, semantic enablement, size)
  • What are the technology options for each core platform, and how can you integrate them?
Questions to the Panel of Speakers and Delegates move to the Seminar Rooms
Seminar Sessions

(To view topics see the seminars page)

Networking Lunch Served in the Exhibition Area

Session TWO: Making Data the Centrepiece of your Business

  • Exploring common pitfalls and how to avoid these
  • Solving Critical Challenges and Fulfilling your Strategic Vision
  • Cultivating a data-driven culture, people, and skills
  • Managing and implementing a secure and scalable Big Data architecture
Conference Chair’s Afternoon Address
Case Study – Unleashing the Power of Customer Analytics

Peter Revill, Data Scientist,

Customer analytics is one of the principal drivers of big data analytics adoption. Yet, the sheer variety of potential opportunities and applications to deliver excellent customer experience is overwhelming. We look at:

  • Key trends and best practices in customer analytics
  • Personalisation and customer journey analytics
  • How partnering with service providers can help you mature your customer analytics
  • Adapting tools and strategies to fit your business (e.g. supply chain and merchandising applications, chatbots, CDP)
Case Study – Discovering Quants: The Wolf Data Scientists of Wall Street

The need for quantitative finance expertise is increasingly growing, and it is clear that the role of the “quant” – quantitative analysts – has changed significantly. New tasks need new skills. Alternative data, crypto, AI and blockchain have all opened whole new avenues for quantitative analytics to expand.

We explore the challenges/strategies that “quants” face from applying AI and machine learning to a variety of quantitative finance issues (e.g. risk management, trading) to the use of alternative data for forecasting purposes.

Telecoms case study: Driving analytics adoption in telco

Kieran Keene, Data Engineer – Architect, Telefonica

Data drives business decisions and huge business benefit. We all know it, but very few of us do anything about it. During this session, we’ll discuss the reasons why people don’t pursue valuable data and look at ways we can drive adoption across the business and empower users to start uncovering valuable insights from the data the business holds while maintain the level of governance required to meet regulatory demands.

Every business can be data driven; the first step is empowering your team.

  • Why don’t people adopt data?
  • How do we encourage / empower them to do so?
  • How do we remove red tape & maintain data governance?
Questions to the Panel of Speakers
Afternoon Networking and Refreshments served in the Exhibition Area
Responsibility and transparency in recommendation engines

Richard Bownes, Data Scientist, BBC

Recommendation engines are built into consumption platforms to improve engagement, increase profits, click through, journey length, viewing duration, etc. In order to make personal recommendations more targeted, these often require some degree of personal information.

At the BBC Datalab, as a public service entity, we have a responsibility to use this data efficiently, transparently and always legally compliantly. Further complicating our mission, we have editorial obligations and considerations in the content we surface.

  • Public service recommendations have a different set of parameters to adhere to
  • The balance between editorial standards, privacy considerations and good recommendations
  • Maintaining a level of trust and transparency while surfacing personally generated recommendations
Augmented Analytics: I have data,now what?

Augmented analytics has emerged as a potential solution for this widespread problem of turning vast troves of data into meaningful insights. We explore:

  • What is augmented analytics, and why should I invest in it?
  • What are the roadblocks? Assessing technology challenges, investment risks and lack of skills.
  • Exploring early successful stories (e.g. the medical industry training programmes)
  • Application strategies: extracting complex patterns, semantic indexing, data tagging, simplifying discriminative tasks and more
2020. Have We Achieved What Was Predicted?

2020 is here. A plethora of predictions was written regarding Big Data for this year. Have we met any of the expectations? Were the predictors, right? Has IoT finally integrated with Big Data? Have automated analytics finally changed the way we read data? Has GDPR forced companies to discard stored Data? Has a business with data-driven approaches won as much as it was predicted?

We explore the current landscape and forecast what the next decade may hold.

Do you want to place your bet?

Questions to the Panel of Speakers
Closing Remarks from the Conference Chair
Conference Closes

Please note:
Whitehall Media reserve the right to change the programme without prior notice.